Чтение и запись файлов CSV на Python

name,department,birthday month
John Smith,Accounting,November
Erica Meyers,IT,March

import csv

with open('employee_birthday.txt') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    line_count = 0
    for row in csv_reader:
        if line_count == 0:
            print(f'Column names are {", ".join(row)}')
            line_count += 1
        else:
            print(f'\t{row[0]} works in the {row[1]} department, and was born in {row[2]}.')
            line_count += 1
    print(f'Processed {line_count} lines.')

Column names are name, department, birthday month
    John Smith works in the Accounting department, and was born in November.
    Erica Meyers works in the IT department, and was born in March.
Processed 3 lines.

name,department,birthday month
John Smith,Accounting,November
Erica Meyers,IT,March

import csv

with open('employee_birthday.txt', mode='r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    line_count = 0
    for row in csv_reader:
        if line_count == 0:
            print(f'Column names are {", ".join(row)}')
            line_count += 1
        print(f'\t{row["name"]} works in the {row["department"]} department, and was born in {row["birthday month"]}.')
        line_count += 1
    print(f'Processed {line_count} lines.')

Column names are name, department, birthday month
    John Smith works in the Accounting department, and was born in November.
    Erica Meyers works in the IT department, and was born in March.
Processed 3 lines.

name,address,date joined
john smith,1132 Anywhere Lane Hoboken NJ, 07030,Jan 4
erica meyers,1234 Smith Lane Hoboken NJ, 07030,March 2

import csv

with open('employee_file.csv', mode='w') as employee_file:
    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)

    employee_writer.writerow(['John Smith', 'Accounting', 'November'])
    employee_writer.writerow(['Erica Meyers', 'IT', 'March'])

John Smith,Accounting,November
Erica Meyers,IT,March

import csv

with open('employee_file2.csv', mode='w') as csv_file:
    fieldnames = ['emp_name', 'dept', 'birth_month']
    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)

    writer.writeheader()
    writer.writerow({'emp_name': 'John Smith', 'dept': 'Accounting', 'birth_month': 'November'})
    writer.writerow({'emp_name': 'Erica Meyers', 'dept': 'IT', 'birth_month': 'March'})

emp_name,dept,birth_month
John Smith,Accounting,November
Erica Meyers,IT,March

Name,Hire Date,Salary,Sick Days remaining
Graham Chapman,03/15/14,50000.00,10
John Cleese,06/01/15,65000.00,8
Eric Idle,05/12/14,45000.00,10
Terry Jones,11/01/13,70000.00,3
Terry Gilliam,08/12/14,48000.00,7
Michael Palin,05/23/13,66000.00,8

import pandas
df = pandas.read_csv('hrdata.csv')
print(df)

             Name Hire Date   Salary  Sick Days remaining
0  Graham Chapman  03/15/14  50000.0                   10
1     John Cleese  06/01/15  65000.0                    8
2       Eric Idle  05/12/14  45000.0                   10
3     Terry Jones  11/01/13  70000.0                    3
4   Terry Gilliam  08/12/14  48000.0                    7
5   Michael Palin  05/23/13  66000.0                    8

>>> print(type(df['Hire Date'][0]))

import pandas
df = pandas.read_csv('hrdata.csv', index_col='Name')
print(df)

               Hire Date   Salary  Sick Days remaining
Name
Graham Chapman  03/15/14  50000.0                   10
John Cleese     06/01/15  65000.0                    8
Eric Idle       05/12/14  45000.0                   10
Terry Jones     11/01/13  70000.0                    3
Terry Gilliam   08/12/14  48000.0                    7
Michael Palin   05/23/13  66000.0                    8

import pandas
df = pandas.read_csv('hrdata.csv', index_col='Name', parse_dates=['Hire Date'])
print(df)

                Hire Date   Salary  Sick Days remaining
Name
Graham Chapman 2014-03-15  50000.0                   10
John Cleese    2015-06-01  65000.0                    8
Eric Idle      2014-05-12  45000.0                   10
Terry Jones    2013-11-01  70000.0                    3
Terry Gilliam  2014-08-12  48000.0                    7
Michael Palin  2013-05-23  66000.0                    8

>>> print(type(df['Hire Date'][0]))

import pandas
df = pandas.read_csv('hrdata.csv',
            index_col='Employee',
            parse_dates=['Hired'],
            header=0,
            names=['Employee', 'Hired','Salary', 'Sick Days'])
print(df)

                    Hired   Salary  Sick Days
Employee
Graham Chapman 2014-03-15  50000.0         10
John Cleese    2015-06-01  65000.0          8
Eric Idle      2014-05-12  45000.0         10
Terry Jones    2013-11-01  70000.0          3
Terry Gilliam  2014-08-12  48000.0          7
Michael Palin  2013-05-23  66000.0          8

import pandas
df = pandas.read_csv('hrdata.csv',
            index_col='Employee',
            parse_dates=['Hired'],
            header=0,
            names=['Employee', 'Hired', 'Salary', 'Sick Days'])
df.to_csv('hrdata_modified.csv')

Employee,Hired,Salary,Sick Days
Graham Chapman,2014-03-15,50000.0,10
John Cleese,2015-06-01,65000.0,8
Eric Idle,2014-05-12,45000.0,10
Terry Jones,2013-11-01,70000.0,3
Terry Gilliam,2014-08-12,48000.0,7
Michael Palin,2013-05-23,66000.0,8